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Forest regeneration is the process by which new tree seedlings become entrenched after forest trees have been harvested or destroyed by fire, insects, or diseases. The process is key to sustainable forestry and can be accomplished through natural regeneration, which occurs when new seedlings or sprouts are produced by trees left on or near the site. The study is to calculate the regeneration status of the tree species in community-managed forests. The sampling points were generated by using the random table. The sample quadrate size was taken 20m×20m for a tree, 5m×5m for a sapling, and 1m×1m for the seedling. In five community forests, altogether 30 quadrates were taken for a tree, 60 nested quadrates were taken for a sapling and 120 nested quadrates were taken for a seedling. The tree layer, sapling layer and seedling layer were differentiated by taking reference tothe Diameter at Breast Height (DBH) class. An individual having DBH ≥ 5cm is considered a tree, an individual having DBH value between 1cm to < 5cm is considered a sapling, and individuals having 1cm to < 5cm DBH are considered as a seedling. The DBH distribution curve was obtained by plotting diameter class in x- axis and density on y- axis. All studied community forests are mixed forest dominated by tree species Castanopsis indica, Diospyros embryopteris (Diospyros malabarica), Shorea robusta, and Schima wallichii (Schima wallichii choisy). The regeneration potential of the Tilahar community forest(CF) is found high despite high grazing and other human intervention. The DBH class distribution curve of tree species is found as inverse J-shape, indicating that the overall forest has a sustainable regeneration.

Drought is one of the most challenging climatic events. Recently, the drought influence in East Africa (EA) total water storage (TWS) is a serious problem, particularly in arid areas with modified natural vegetation relying on water deficit, garnered extensive research interest. Hydro-climatological and vegetation indices and remote sensing datasets derived from Gravity Recovery Climate Experiment (GRACE) mission datasets reveal good performance in analyzing hydrological drought influences in water storage. Over the last decades, studies were considered successful in monitoring the drought influence in the region TWS potential. However, several challenges remained unsolved, hindering the hydrological drought mitigation strategies. This review deals with an overview of drought impact monitoring targeted at the TWS variation with the response of vegetation change for sustainable drought mitigation. To improve the flexibility and adaptive capacities of the water deficit problem, we aim to provide an overview of drought impacts on TWS in the region to redefine the hydro-climatological and vegetation drought indices and improve the understanding of drought impact through remote sensing datasets. This review presents the challenges and prospects and offers a conclusion. Although, we hope that the review can facilitate further study regarding future hydrological drought projection in the development of several scientific research in the field.

Monsoon floods are frequent in the Tarai region of Nepal and claim thousands of lives and substantial numbers of properties every year. Certain human activities are more affected than others in the case of the same hazard. This study analyzes vulnerability to flooding among Tharu households. Data were collected by employing household surveys, group discussions, and key informant interviews in the Thapapur Village Development Committee (VDC) of Kailali district, western Tarai, Nepal. The analysis presented in this study is based on the theory that underpins the pressure and release (PAR) and access models. The results show that Tharu people are the major inhabitants in the study area and they prefer to live within their community; many ex-bonded laborers (marginalized people) choose this location for residence. Human causalities have been reduced in recent years due to easy access to cell phones, which has facilitated effective flood warnings with suitable lead times, but agriculture production loss and other losses are still high. Agricultural land is not only an important natural asset but is also considered a financial asset due to its high price and private ownership. The study concludes that subsistence agriculture-based households with small landholding sizes and less income diversification are highly vulnerable to flooding. Improper resettlement of ex-bonded laborers and land fragmentation due to separation of family members are the most prominent factors resulting in small landholdings. The results can guide government authorities to develop proper flood management strategies for the people living in the lowlands (particularly the Tarai region) of Nepal.

An important but often overlooked feature of Diels-Alder (DA) cycloadditions is the ability for DA adducts to undergo mechanically induced cycloreversion when placed under force. Herein, we demonstrate that the commonly employed DA cycloaddition between furan and maleimide to crosslink hydrogels results in slow gelation kinetics and "mechanolabile" crosslinks that relate to reduced material strength. Through rational computational design, "mechanoresistant" DA adducts were identified by constrained geometries simulate external force models and employed to enhance failure strength of crosslinked hydrogels. Additionally, utilization of a cyclopentadiene derivative, spiro[2.4]hepta-4,6-diene, provided mechanoresistant DA adducts and rapid gelation in minutes at room temperature. This study illustrates that strategic molecular-level design of DA crosslinks can provide biocompatible materials with improved processing, mechanical durability, lifetime, and utility.

Migration is one of the major factors affecting population distribution of Nepal. Job opportunity, education facility, medical facility and topographic situation are the key causes of migration. Human movement from mountain to hill; hill to Tarai; and rural to urban area migration are the major trend observed in Nepal. Migration has direct impact on various sectors including financial, social, demographic, and biological. All kind of services including educational, medical, transformational, and safety services should make equally accessible over the various parts of the country. This research helps to understand the trend of internal and international labor migration in Nepal. The study has used secondary data from Department of Foreign Employment (DoFE), Central Bureau of Statistics (CBS), Ministry of Finance (MoF). Remittance is playing a crucial role in the Nepalese economy. The highest foreign labor migration was found in the year 2013/14. The trend was continuously increased until 2013/14 and it has stated decreasing thereafter. International labor migration has mostly taken place to Malaysia, Qatar, Saudi Arabia, 110 UAE and other Southeast Asian countries and it is growing over the time that has shifted the agricultural based economy towards remittance-based economy.

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30–40 cm and 90–100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R2 of 0.642, RMSE of 0.283, and MAE of 0.213 at 30–40 cm depth, and with the R2 of 0.450, RMSE of 0.276, and MAE of 0.220 at 90–100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher; in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30–40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90–100 cm showed fluctuating increasing trend.

Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than -1.08 and -1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6.

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Research on flood disaster generate ideas and provoke the best solution for disaster management. This work primarily focuses research on monsoon flood due to its frequency and severity in the southern flood plain of Nepal. Here we review the previous studies on flood disaster at the regional and national level and compare with the global context. This facilitates exploring the data and methods that are mostly unexplored, and areas that have not lightened in the field of flood studies in Nepal. Our scope of literature review limited the literature that are accessed through internet. The findings are revised and compared with different contexts. Multi-criteria weighted arithmetic mean have been used to find the spatial severity of flood disaster in 2017. We found several studies carried out on flood in Nepal. They are mostly based on field-based data, except few that have used current state-of-art, remote sensing method, using satellite images. Since the multi-spectral optical satellite imageries have a high cloud effect, it is not very useful in real time flood mapping; and very limited Synthetic-Aperture Radar (SAR) image, has been used in Nepal. In Global context, Support Vector Machine and Random Forest method are used in flood risk assessment; VNG flood V1.0 software has been used in flood forecasting, and Probabilistic Change Detection and Thresholding have widely been used in flood research, which can also be adopted in Nepalese context.

This paper tried to highlights impact of flood disaster on local livelihood in Kandra river basin, Kailali District. The study also examined coping strategies of the members of flood affected village community. In so doing, required data and information were collected and generated through household survey, semi-structured interviews as well as focus group discussions, key informant interview and observations. There are almost 50 settlements in the river basin; whereas 15 settlements are along with Kandra River. Three settlements: Kanhaiyapur, Thengarpuruwa and Lalbojhi; are most frequently affected from flood disaster that were selected for the study purpose. Thirty households from each settlement have been selected for household questionnaire. Satellite images and aerial photos are used to analyze terrain, channel course. The study finds that all communities living in the low-lying area and along the riverbank are affected from flood disaster. However, not all people are vulnerable equally. To cope with this problem, local people have designed and practicing various adaptation techniques. The adaptation strategy differs based on their socio-economic condition, such as education, income, occupations and living conditions. Establishment of early warning system and capacity building of flood victims will help to cope with disasters.

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The annual total NPP of Mongolia showed a slight increment with an annual increase of 0.0007 Pg (0.68 g C per meter square) from 2003 to 2018 (p = 0.82) due to the changes in climatic parameters and land cover change. Likewise, high increments per unit area found in forest NPP, while decreased NPP trend was observed in the shrubland. In conclusion, among the three climatic parameters, temperature was the factor with the largest influence on NPP variations (r = 0.917) followed precipitation (r = 0.825), and net radiation (r = 0.787). Forest and wetland NPP had a low response to precipitation, while inter-annual NPP variation shows grassland, shrubland, and sparse vegetation were highly sensitive rangeland types to climate fluctuations.

Understanding the development mechanism of drought events, characterization of future drought metrics, and its impact on crop yield is crucial to ensure food security globally, and more importantly, in South Asia. Therefore, the present study assessed the changes in future projected drought metrics and evaluated the future risk of yield reduction under drought intensity. We characterized the magnitude, intensity, and duration of future drought by means of the SPEI drought index using CMIP6 (Coupled Model Inter-comparison Phase-6) climate models. The impact of future drought on crop yield was quantified from the ISI-MP (Inter-Sectoral Impact Model Inter-comparison Project) crop model by a proposed non-linear ensemble of Random Forest (RF) and Gradient Boosting Machine (GBM). Results suggested that high drought magnitude with a longer drought duration is projected in some regions of South Asia while high drought intensity comes with a shorter duration. It was also found that Afghanistan, Pakistan, and India will experience a longer drought duration in the future. Our proposed ensemble machine learning (EML) approach had high predictive skill with a minimum value of RMSE (0.358-0.390), MAE (0.222-0.299), and a maximum value of R2 (0.705-0.918) compared to the stand-alone methods of RF and GBM for yield loss risk projection. The drought-driven impact on crop yield demonstrates a high risk of yield loss under extreme drought events, which will encounter 54.15%, 29.30%, and 50.66% loss in the future for rice, wheat, and maize crops, respectively. Furthermore, drought and yield loss risk dynamics suggested a one unit decrease in SPEI value would lead to a 14.2%, 7.5%, and 10.9% decrease in yield for rice, wheat, and maize crops, respectively. This study will provide a notable direction for policy agencies to build resistance to crop production against the drought impact in the regions that are critical to climate change.

Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches&mdash, Crichton&rsquo, s risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton&rsquo, s risk triangle has a prominent representation for regions with high risks. The stepwise multiple linear regression zero-order correlation coefficient between HVI and outdoor workers is 0.715, highlighting the vulnerability of this particular group. Spearman&rsquo, s rho nonparametric correlation and the mean test reveals that heat risk index is strongly correlated with HVI in most of the main urban regions in the study area, with a significantly lower value than the latter. The analysis of variance shows that the distribution of HVI exhibits greater variety across urban regions than that of heat risk index. Our research provides new insight into heat risk assessment for further study of heat health risk in developing countries.